> ## Documentation Index
> Fetch the complete documentation index at: https://docs.promptlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Getting Started
The overall process of building an evaluation pipeline looks like this:
1. **Select Your Dataset**: Choose or upload datasets to serve as the basis for your evaluations, whether for scoring, regression testing, or bulk job processing.
2. **Build Your Pipeline**: Start by visually constructing your evaluation pipeline, defining each step from input data processing to final evaluation.
3. **Run Evaluations**: Execute your pipeline, observe the results in a spreadsheet-like interface, and make informed decisions based on comprehensive metrics and scores.
## Creating a Pipeline
1. **Initiate a Batch Run**: Start by creating a new batch run, which requires specifying a name and selecting a dataset.
2. **Dataset Selection**: Upload a CSV/JSON dataset, or create a dataset from historical data using filters like time range, prompt template logs, scores, and metadata. [Learn more here.](/features/evaluations/datasets-overview)
You now have a pipeline. Preview mode allows you to iterate with live feedback, allowing for adjustments in real-time.
## Setting up the Pipeline
### Adding Steps
Click 'Add Step' to start building your pipeline, with each column representing a step in the evaluation process.
Steps execute in order left to right. That means that if a column depends on a previous column, make sure it appears to the right of the dependency.
#### Common Step Types
* **Prompt Template**: Select a prompt template from the registry, set model parameters, LLM, arguments, and template version.
* **Custom API Endpoint**: Define a URL to send and receive data, suitable for custom evaluators or external systems.
* **Human Input**: Engage human graders by adding a step that allows for textual input.
* **String Comparison**: Use this step to compare the outputs of two previous step, showing a visual diff when relevant.
* **LLM Assertion**: Use an AI judge to score whether an output satisfies a natural-language criterion.
For model comparison, add multiple **Prompt Template** columns that use the same prompt with different model overrides. See [Compare Models](/onboarding-guides/compare-models).
#### Scoring
If the last step of your evaluation pipeline contains all booleans or numeric values, that will be consider the score for the row. Your full evaluation report will have a scorecard of the average of this last step.
*NOTE: All cells in the last column must be boolean or all must be numeric. If any cell deviates, the score will not be calculated*
## Executing Full Batch Runs
Transition from pipeline to full batch run to apply your pipeline across the entire dataset for comprehensive evaluation.
For model comparison, add multiple **Prompt Template** columns that use the same prompt with different model overrides. See [Compare Models](/onboarding-guides/compare-models).
#### Scoring
If the last step of your evaluation pipeline contains all booleans or numeric values, that will be consider the score for the row. Your full evaluation report will have a scorecard of the average of this last step.
*NOTE: All cells in the last column must be boolean or all must be numeric. If any cell deviates, the score will not be calculated*
## Executing Full Batch Runs
Transition from pipeline to full batch run to apply your pipeline across the entire dataset for comprehensive evaluation.